Anomaly Detection in Network Traffic Using Machine Learning Algorithms

Thesis Abstract

Abstract
Anomaly detection in network traffic using machine learning algorithms is a critical area of research in the field of computer science and cyber security. This thesis explores the application of machine learning techniques to detect anomalies in network traffic data, with the aim of improving the security and performance of computer networks. The increasing complexity and volume of network traffic data make manual analysis and detection of anomalies infeasible, necessitating the development of automated methods based on machine learning algorithms. The study begins with a comprehensive introduction to the research topic, providing background information on network traffic analysis and the challenges associated with anomaly detection. The problem statement highlights the importance of detecting and mitigating network anomalies to enhance network security and performance. The objectives of the study are outlined, focusing on the development and evaluation of machine learning models for anomaly detection in network traffic data. The limitations and scope of the study are discussed, emphasizing the constraints and boundaries within which the research is conducted. The significance of the study is highlighted, emphasizing the potential impact of developing effective anomaly detection techniques on enhancing network security and performance. The structure of the thesis is outlined, providing a roadmap for the subsequent chapters. The literature review chapter presents a comprehensive analysis of existing research and methodologies related to anomaly detection in network traffic. Key concepts, algorithms, and techniques in machine learning for anomaly detection are discussed, providing a foundation for the research methodology chapter. The research methodology chapter outlines the approach and methods used to develop and evaluate machine learning models for anomaly detection in network traffic data. Data collection, preprocessing, feature selection, model training, and evaluation procedures are described in detail, highlighting the experimental setup and performance metrics used to assess the effectiveness of the proposed models. The findings chapter presents the results of the experiments conducted to evaluate the performance of the machine learning models in detecting anomalies in network traffic data. The discussion of findings explores the strengths and limitations of the proposed models, comparing them with existing approaches and identifying areas for further improvement. The conclusion and summary chapter provide a comprehensive overview of the research findings, highlighting the contributions of the study to the field of anomaly detection in network traffic using machine learning algorithms. The implications of the research findings are discussed, along with recommendations for future research directions to advance the state-of-the-art in network security and performance optimization. In conclusion, this thesis contributes to the advancement of anomaly detection techniques in network traffic using machine learning algorithms, offering insights and recommendations for improving the security and performance of computer networks. The research findings have the potential to inform the development of more effective and efficient anomaly detection systems, benefiting organizations and individuals seeking to safeguard their networks against cyber threats.